In this paper, an artificial neural network is proposed for feature extraction of hand
written characters. The learning algorithm is developed based on a proposed modified
SammoníŽs stress for our feedforward neural networks, which can not only minimize intra
class pattern distances but also preserve interclass distances in the output feature
space. The proposed feature extraction method tries to calculate rough classes using a
Competitive Learning neural network, which is an unsupervised neural network. Then
the proposed neural network was used with modified SammoníŽs stress to perform feature
extraction using information obtained by means of a Competitive Learning Network.
The features thus obtained were compared with a standard PCA neural network and a
neural network using SammoníŽs stress in terms of their classification accuracy. Two
numerical criteria were used for performance evaluation of the features íV the normalized
classification error rate and modified SammoníŽs stress. It is found that proposed modified
SammoníŽs stress provides features that are more efficient based on these two numerical
criteria.